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Field
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. The successful candidate will integrate multiscale first-principles modeling, sparse symbolic regression, and adaptive experimental design in self-driving laboratory settings to elucidate both desired catalytic
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preclinical in vitro and in vivo models Supervise and mentor MSc/MD/PhD students Collaborate with national and international research partners Present your results at scientific meetings Publish your findings
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modeling. Our research contributes to robust management concepts for the sustainable provisioning of diverse ecosystem services, and for maintaining the integrity and diversity of ecosystems in a changing
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its role in hematopoiesis over life time. We plan to employ inflammatory challenges and will combine experimental animal models with studies on human cells to translate principles and mechanisms
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Design and implement optimization techniques for full-stack improvement of quantum algorithms Model major sources of experimental error for control theory or for error mitigation techniques Scientific
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at our centre, we actively promote women in science and in leadership positions. We among others do this through our gender equality plan and the cascade model measures which we actively implement
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Ph.D. or equivalent degree in mathematics, physics, computer science, bioinformatics, or a related field Experience in developing deep learning models Ideally, prior experience in analyzing biological
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Max Planck Institute for Brain Research, Frankfurt am Main | Frankfurt am Main, Hessen | Germany | 2 months ago
A doctoral degree in neuroscience or a related STEM field Prior experience in systems neuroscience using animal models A solid foundation in quantitative data analysis and statistics Programming
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multi-disciplinary approaches to answer these key questions including; immunology, oncology (in vitro model-organoid systems, ex vivo tissue culture), microbiology, next generation sequencing (16S seq
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characterization of interface deformation mechanisms, including advanced SEM and TEM methods Finite Element Modeling to design and optimize micromechanical testing geometries Timely dissemination of research